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DOI: 10.14569/IJACSA.2023.01407118
PDF

Deep-Learning-based Analysis of the Patterns Associated with the Changes in the Grit Scores and Understanding Levels of Students

Author 1: Ayako OHSHIRO

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 7, 2023.

  • Abstract and Keywords
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Abstract: The purpose of this study is to classify the pattern of the understanding level changes for university students during class term, and analyze the relation between them and the changes in the Grid score before and after the class term. Dynamic time warping was applied for classification of the understanding level, and the decision tree was applied to analyze the relation between the changes in the understanding level and that in the Grid score. As a result, it was shown that a large variety of the patterns of changes in the understanding level, and the relations between the understanding level and Grid score cover a wide variety, too. It is necessary to take these results for conducting effective lectures.

Keywords: Time series; dynamic time warping; decision tree; Grit

Ayako OHSHIRO, “Deep-Learning-based Analysis of the Patterns Associated with the Changes in the Grit Scores and Understanding Levels of Students” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01407118

@article{OHSHIRO2023,
title = {Deep-Learning-based Analysis of the Patterns Associated with the Changes in the Grit Scores and Understanding Levels of Students},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01407118},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01407118},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Ayako OHSHIRO}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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